skip to main content

Search for: All records

Creators/Authors contains: "Ong, Shyue Ping"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Free, publicly-accessible full text available June 1, 2023
  2. Free, publicly-accessible full text available May 10, 2023
  3. Developing chemically and thermally stable, highly efficient green-emitting inorganic phosphors is a significant challenge in solid-state lighting. One accessible pathway for achieving green emission is by forming a solid solution with superior blue-emitting materials. In this work, we demonstrate that the cyan-emission ( λ em = 481 nm) of the BaScO 2 F:Eu 2+ perovskite can be red-shifted by forming a solid solution following (Ba 1− x Sr x ) 0.98 Eu 0.02 ScO 2 F ( x = 0, 0.075, 0.15, 0.25, 0.33, 0.40). Although green emission is achieved ( λ em = 516 nm) as desired, the thermalmore »quenching (TQ) resistance is reduced, and the photoluminescence quantum yield (PLQY) drops by 65%. Computation reveals the source of these changes. Surprisingly, a basic density functional theory analysis shows the gradual Sr Ba substitution has negligible effects on the band gap ( E g ) energy, suggesting the activation energy barrier for the thermal ionization quenching remains unchanged, while the nearly constant Debye temperature indicates no loss of average structural rigidity to explain the decrease in the PLQY. Instead, temperature-dependent ab initio molecular dynamics (AIMD) simulations show that gradual changes of the Eu 2+ ion's local coordination environment rigidity are responsible for the drop in the observed TQ and PLQY. These results express the need to computationally analyze the local rare-earth environment as a function of temperature to understand the fundamental origin of optical properties in new inorganic phosphors.« less
    Free, publicly-accessible full text available February 24, 2023
  4. Abstract

    Predicting properties from a material’s composition or structure is of great interest for materials design. Deep learning has recently garnered considerable interest in materials predictive tasks with low model errors when dealing with large materials data. However, deep learning models suffer in the small data regime that is common in materials science. Here we develop the AtomSets framework, which utilizes universal compositional and structural descriptors extracted from pre-trained graph network deep learning models with standard multi-layer perceptrons to achieve consistently high model accuracy for both small compositional data (<400) and large structural data (>130,000). The AtomSets models show lowermore »errors than the graph network models at small data limits and other non-deep-learning models at large data limits. They also transfer better in a simulated materials discovery process where the targeted materials have property values out of the training data limits. The models require minimal domain knowledge inputs and are free from feature engineering. The presented AtomSets model framework can potentially accelerate machine learning-assisted materials design and discovery with less data restriction.

    « less
  5. Abstract

    The L-edge X-ray Absorption Near Edge Structure (XANES) is widely used in the characterization of transition metal compounds. Here, we report the development of a database of computed L-edge XANES using the multiple scattering theory-based FEFF9 code. The initial release of the database contains more than 140,000 L-edge spectra for more than 22,000 structures generated using a high-throughput computational workflow. The data is disseminated through the Materials Project and addresses a critical need for L-edge XANES spectra among the research community.

  6. null (Ed.)